“For every 38,000 ChatGPT queries, that is the same amount of water that is used in the production of a single almond in California,” OpenAI CEO Sam Altman says. That estimate also likely uses a low figure for ChatGPT water consumption and a higher estimate for almond production.
Still, it is reasonable to state that an almond takes thousands to tens of thousands of times more water to produce than a single ordinary AI query.
Comparison | Water |
California almond — total water footprint | ~12 liters |
California almond — freshwater use | ~5.8 to 6 L |
ChatGPT query — OpenAI/Altman estimate | ~0.32 mL |
12 L ÷ 0.32 mL | ~37,500 queries |
6 L ÷ 0.32 mL | ~18,750 queries |
Other studies, though, do tend to validate the water usage required as between five liters per kilogram of almonds to 13,000 liters of water per kilogram of almonds. There are other methodological issues including how one calculates water consumption used to produce electricity, for example.
And some of the water used to produce almonds comes from rainfall, which some might not consider a draw on water supplies, though others would likely counter that such water might be used in other ways (to grow different crops; recharge aquifers, flow to rivers and so forth).
Study/source | Subject | Water-use estimate | What it tells us |
Fulton, Norton & Shilling, 2019, Ecological Indicators | California almonds | 10,240 L/kg, or ~12 L/almond | Comprehensive water-footprint accounting: blue + green + gray water. (DOI) |
Mekonnen & Hoekstra, 2011, Hydrology and Earth System Sciences | Global crop water footprints | California almonds: ~12,984 L/kg in their underlying dataset | Establishes the influential global water-footprint methodology and distinguishes green, blue and gray water. (HESS) |
Marvinney & Kendall, 2021, International Journal of Life Cycle Assessment | California almonds | ~4,820 L freshwater/kg; ~4,540–5,150 L depending on region | A more direct freshwater-use/LCA measure, excluding rainfall and treating water differently from the broader water-footprint approach. (Springer) |
Wong et al., 2021, Water Resources Research | California Central Valley crops | Almonds accounted for 22.2% of crop consumptive water use in water year 2014 | Shows that the issue isn't merely water per almond: almonds were the largest single crop category in Central Valley consumptive water use in that year. (AGU Journals) |
Stewart et al., 2011, California Agriculture | Almond irrigation | Deficit irrigation saved about 5 inches of water/year without significant yield reduction | Demonstrates that almond water intensity isn't fixed; irrigation technology and management can materially change it. (California Agriculture) |
Li et al./Ren et al., 2023–25, Making AI Less "Thirsty" | GPT-3 | ~700,000 L direct water for training; ~5.4 million L including broader water footprint | Demonstrates that AI's water footprint includes both inference and the much larger infrastructure/training question. (DOI) |
Google, 2025, production Gemini measurement | Gemini text inference | 0.26 mL/prompt comprehensive measurement | One of the few production-scale measurements rather than a theoretical estimate. (Google Cloud) |
Green, 2026, The Hidden Thirst of AI | GPT-4o and Gemini | GPT-4o: ~0.75 mL total in its reference scenario; published ChatGPT figure 0.322 mL | Illustrates how including electricity-generation water can substantially increase the apparent AI footprint. (MDPI) |
On the computing consumption front, some might argue the water consumed to build and operate data centers has to be counted, as well as the water to produce servers, connectors and cables, for example.
So one might plausibly argue that producing one almond is equivalent to as few as 350 prompts or as many as 23,000.
AI water estimate | Equivalent to one almond |
0.26 mL — Google's measured Gemini prompt | ~23,000 prompts |
0.32 mL — OpenAI/Altman ChatGPT estimate | ~18,750 prompts |
0.75 mL — GPT-4o broader estimate including indirect water | ~8,000 prompts |
6 mL — higher-end estimate for some AI tasks | ~1,000 prompts |
17 mL — high-end GPT-4o estimate in a recent study | ~350 prompts |
That provides some perspective on the “AI data centers use too much water” claim, but also highlights the water intensity of almond growing. Compared to growing lettuce, almonds require 6690 percent more water, for example.
Crop | Green water | Blue water | Gray water | Total | Almonds vs. crop |
Almonds, shelled | 9,264 L | 3,816 L | 3,015 L | 16,095 L | — |
Pistachios | 3,095 | 7,602 | 666 | 11,363 L | 42% more |
Cashews | 12,853 | 921 | 444 | 14,218 L | 13% more |
Walnuts, shelled | 5,293 | 2,451 | 1,536 | 9,280 L | 73% more |
Lentils | 4,324 | 489 | 1,060 | 5,874 L | 174% more |
Dry beans | 3,945 | 125 | 983 | 5,053 L | 218% more |
Sorghum | 2,857 | 103 | 87 | 3,048 L | 428% more |
Soybeans | 2,037 | 70 | 37 | 2,145 L | 650% more |
Wheat | 1,277 | 342 | 207 | 1,827 L | 881% more |
Rice, paddy | 1,146 | 341 | 187 | 1,673 L | 962% more |
Maize/corn | 947 | 81 | 194 | 1,222 L | 1,217% more |
Potatoes | 191 | 33 | 63 | 287 L | 5,508% more |
Tomatoes | 108 | 63 | 43 | 214 L | 7,521% more |
Sugar cane | 139 | 57 | 13 | 210 L | 7,665% more |
Lettuce | 133 | 28 | 77 | 237 L | 6,690% more |
Roughly the same argument, though, can be made about agricultural water use and computing water use. Nationally, farms consumed nearly 30 trillion gallons of water for crop irrigation in 2020, the most recent data available from the U.S. Geological Survey. Meanwhile, data centers nationwide used about 228 billion gallons of water in 2023.
Yes, data centers consume water, though other alternatives provide ways of consuming less water. But data centers consume vastly less water than agriculture: hundreds to many thousands of times more water, in fact.
Compared to other necessary uses, data center water use is miniscule, in fact.
source: Axios
In the intermountain U.S. west, all industry combined consumes five percent to 10 percent of water, while agriculture consumes 70 percent to 80 percent, for example.
Sector | Share of Water Consumption (Typical Western Basin) |
Agriculture | 70–80% |
Municipal | 10–20% |
Industry | 5–10% |
